In contrast to the approaches described above, there are models that do not introduce explicitly a distribution for partitions of contiguous clusters. For example, [1]} considered a procedure for detecting change points by minimizing a cost function, which detects the optimal number and location of change points with a linear computational complexity under mild conditions. [2]} also minimize a cost function when searching for change points in cross-correlated processes. [3]} proposed a robust nonparametric method using a divergence measure based on Euclidean distances. With this method, the authors showed that it is possible to detect any distributional change within an independent sequence of random variables without making any distributional assumption beyond the existence of the \(\alpha \) -th absolute moment, for some \(\alpha \in (0,2)\) . [4]} proposed a novel change point detection algorithm based on the Kolmogorov–Smirnov statistic and showed that it is nearly minimax rate optimal under suitable conditions.
